Unraveling Fraud Networks

A financial institution or credit card company. Case Study: Graph-based Techniques in Action The Client The Challenge

The deployment of a real-time fraud detection system capable of accurately identifying fraudulent transactions. This proactive approach enables the financial institution to initiate timely countermeasures, mitigating risks. The client is grappling with detecting and preventing fraudulent transactions within their credit card platform. Their goals are twofold — to curtail financial losses and to shield their customers from unauthorized charges.

The Proposed Solution

To illuminate complex transactional relationships, which are instrumental in detecting potential fraudulent behavior.

Fractal's Role

Our approach follows a logical progression from extracting the relevant data to evaluating the results.

Handling null values, handling categorical values, dropping off unnecessary features

Tabular Dataset to Graph Networks using networkx library

Finding edge weight distribution, node degree distribution, centralities etc.

Credit Card dataset

Data Extraction

Data pre-processing

Graph Network

Exploratory Data Analysis

Evaluating the performance of the model

Model training using classifiers & train - te st split using stratified k-fold

Handling categorical values using one hot encoding, standardizing the features

Evaluation

Model Building

Final Data Preparation

To accurately reflect the connections between customers and their transactions, Fractal creates graphs that are structured as follows:

Nodes: These represent the credit card number and merchant. Edges denote transactions between the credit card number and the merchant. Edge Weight: This signifies the transaction's magnitude or amount.

When graph features are incorporated into the model, they emerge as the most influential factors. In our case study, an increase in accuracy was noted, with the Area Under the Curve (AUC) metric rising from 0.72 to 0.76, reflecting an increase of nearly 6%.

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